Method and system for data communication

ABSTRACT

Aspects of the present disclosure are directed to electronic computer implemented methods of data communication. The method includes receiving a plurality of virtual nodes with EDI data payload including a node attribute, a GPS location attribute and a biometric ID attribute and at least one data element associated with the GPS location attribute. In further aspect, the method includes electronically via a data communications network, processing the EDI data payloads including the node attribute, the GPS location attribute and the biometric ID attribute and the at least one data element associated with the GPS location attribute. Then, electronically processing the EDI data payloads in a network with machine learning and providing an electronic message responsive thereto. In yet a further aspect, the data communications method includes a step of processing the smart data set in the network with machine learning and providing an electronic message responsive thereto.

BACKGROUND

Moore's law predicted that the number of transistors on a computer chipwould double every two years while the chip's price would remainconstant. “Moore's law” meant consumers could buy the same technologytwo years later for about the same price. Fifty years later, Moore's lawprediction has endured to the idea that technology companies haverecognized Moore's law as a benchmark they must meet, or fall behind inthe market. Patrons have come to expect technological products to befaster, cheaper, and more compact over time. This expectation seems tohave driven trends of rapid growth in computing power, smaller devices,the ability to connect to the Internet, and reduction in cost and bigdata. There is a need to improve the technological processing in the newcomputing era.

SUMMARY

In light of the foregoing background, the following presents asimplified summary of the present disclosure in order to provide a basicunderstanding of some aspects of the disclosure. This summary is not anextensive overview of the disclosure. It is not intended to identify keyor critical elements of the disclosure or to delineate the scope of thedisclosure. The following summary merely presents some concepts of thedisclosure in a simplified form as a prelude to the more detaileddescription provided below.

Aspects of the present disclosure are directed to electronic computerimplemented methods of data communication. In one aspect, the methodincludes, via a computer-based network, receiving a plurality of virtualnodes with EDI data payload including a node attribute, a GPS locationattribute and a biometric ID attribute and at least one data elementassociated with the GPS location attribute. In further aspect, themethod includes electronically via a data communications network,processing the EDI data payloads including the node attribute, the GPSlocation attribute and the biometric ID attribute and the at least onedata element associated with the GPS location attribute. Thenelectronically processing the EDI data payloads in a network withmachine learning and providing an electronic message responsive thereto.In yet further aspects, includes a step of transmitting via an EDI datapayload the electronic message to a device associated with the biometricID attribute. In yet a further aspect, the method includes outputting asubset of the EDI data payloads to define a smart data set. In yet afurther aspect, the data communications method includes a step ofprocessing the smart data set in the network with machine learning andproviding an electronic message responsive thereto.

Aspects of the present disclosure are directed a digital computersystem, comprising: at least one computer readable database configuredto maintain a plurality of computer readable nodes; and at least onecomputing device, operatively connected to the at least one computerreadable database, configured to: electronically receive a plurality ofvirtual nodes with EDI data payload including a node attribute, a GPSlocation attribute and a biometric ID attribute and at least one dataelement associated with the GPS location attribute; electronicallyprocess the EDI data payloads including the node attribute, the GPSlocation attribute and the biometric ID attribute and the at least onedata element associated with the GPS location attribute; andelectronically process the EDI data payloads in a network with machinelearning and providing an electronic message responsive thereto.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. The Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of aspects of the present disclosure andthe advantages thereof may be acquired by referring to the followingdescription in consideration of the accompanying drawings, in which likereference numbers indicate like features, and wherein:

FIG. 1 illustrates a schematic diagram of a digital computingenvironment in which certain aspects of the present disclosure may beimplemented;

FIG. 2 is an illustrative block diagram of workstations and servers thatmay be used to implement the processes and functions of certainembodiments of the present disclosure;

FIGS. 3A and 3B are an illustrative functional block diagram ofworkstations, database and servers that may be used to implement theprocesses and functions of certain embodiments;

FIG. 3C is an illustrative functional block diagram of a neural networkthat may be used to implement the processes and functions of certainembodiments;

FIG. 4 is an example flow chart of an illustrative method for inaccordance with at least one aspect of the present disclosure;

FIG. 5 is a schematic diagram of a mobile computing device with a GPSdevice in accordance with at least one aspect of the present disclosure;

FIG. 6 is an example block diagram of an illustrative smart data set inaccordance with at least one aspect of the present disclosure;

FIG. 7 is an example block diagram of an illustrative mobile deviceproviding for secure AI-based information on screen display inaccordance with at least one aspect of the present disclosure; and

FIG. 8 is an illustrative functional block diagram of a neural networkwith smart data that may be used to implement the processes andfunctions of certain embodiments.

DETAILED DESCRIPTION

In the following description of the various embodiments, reference ismade to the accompanying drawings, which form a part hereof, and inwhich is shown by way of illustration, various embodiments in which thedisclosure may be practiced. It is to be understood that otherembodiments may be utilized and structural and functional modificationsmay be made.

FIG. 1 illustrates a block diagram of an specific programmed computingdevice 101 (e.g., a computer server) that may be used according to anillustrative embodiment of the disclosure. The computer server 101 mayhave a processor 103 for controlling overall operation of the server andits associated components, including RAM 105, ROM 107, input/outputmodule 109, and memory 115.

Input/Output (I/O) 109 may include a microphone, keypad, touch screen,camera, and/or stylus through which a user of device 101 may provideinput, and may also include one or more of a speaker for providing audiooutput and a video display device for providing textual, audiovisualand/or graphical output. Other I/O devices through which a user and/orother device may provide input to device 101 also may be included.Software may be stored within memory 115 and/or storage to providecomputer readable instructions to processor 103 for enabling server 101to perform various technologic functions. For example, memory 115 maystore software used by the server 101, such as an operating system 117,application programs 119, and an associated database 121. Alternatively,some or all of server 101 computer executable instructions may beembodied in hardware or firmware (not shown). As described in detailbelow, the database 121 may provide centralized storage ofcharacteristics associated with vendors and patrons, allowing functionalinteroperability between different elements located at multiple physicallocations.

The server 101 may operate in a networked environment supportingconnections to one or more remote computers, such as terminals 141 and151. The terminals 141 and 151 may be personal computers or servers thatinclude many or all of the elements described above relative to theserver 101. The network connections depicted in FIG. 1 include a localarea network (LAN) 125 and a wide area network (WAN) 129, but may alsoinclude other networks. When used in a LAN networking environment, thecomputer 101 is connected to the LAN 125 through a network interface oradapter 123. When used in a WAN networking environment, the server 101may include a modem 127 or other means for establishing communicationsover the WAN 129, such as the Internet 131. It will be appreciated thatthe network connections shown are illustrative and other means ofestablishing a communications link between the computers may be used.The existence of any of various well-known protocols such as TCP/IP,Ethernet, FTP, HTTP and the like is presumed.

Computing device 101 and/or terminals 141 or 151 may also be mobileterminals including various other components, such as a battery,speaker, and antennas (not shown).

The disclosure is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well known computing systems, environments, and/orconfigurations that may be suitable for use with the disclosure include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, mobile computing devices, e.g.,smart phones, wearable computing devices, tablets, distributed computingenvironments that include any of the above systems or devices, and thelike.

The disclosure may be described in the context of computer-executableinstructions, such as program modules, being executed by a computer.Generally, program modules include routines, programs, objects,components, data structures, etc. that perform particular tasks orimplement particular computer data types. The disclosure may also bepracticed in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote computer storage mediaincluding memory storage devices.

Referring to FIG. 2, an illustrative system 200 for implementing methodsaccording to the present disclosure is shown. As illustrated, system 200may include one or more workstations 201. Workstations 201 may be localor remote, and are connected by one or more communications links 202 tocomputer networks 203, 210 that is linked via communications links 205to server 204. In system 200, server 204 may be any suitable server,processor, computer, or data processing device, or combination of thesame. Computer networks 203, 201 may incorporate various machineintelligence (MI) neutral network 303 (see FIG. 3A) features ofavailable Tensorflow or Neuroph software development platforms (whichare incorporated by reference herein).

Computer network 203 may be any suitable computer network including theInternet, an intranet, a wide-area network (WAN), a local-area network(LAN), a wireless network, a digital subscriber line (DSL) network, aframe relay network, an asynchronous transfer mode (ATM) network, avirtual private network (VPN), or any combination of any of the same.Communications links 202 and 205 may be any communications linkssuitable for communicating between workstations 201 and server 204, suchas network links, dial-up links, wireless links, hard-wired links, etc.

FIG. 3A-3C illustrate an example of representative infrastructureaccording to some embodiments in the disclosure. The different userdevices 301 a-301 c, via terminals/workstations, electronicallycommunicates with a plurality of different user devices 302 a-302 c,through the cloud-based neutral network processing system 300 includingcomputer network 203, server 305 and electronic database 307.Cloud-based neutral network system 300 may incorporate various neutralnetwork 303 features of available Tensorflow or Neuroph platforms asAPIs, etc. In one embodiment, users execute commands withterminals/workstations to exchange information with the processingsystem 300 such that the identity of the users are shielded from eachother. These terminals may be standard personal computers as are knownin the art. In alternative embodiments, the users may use hand-held,tablet computers or other portable electronic devices, such as smartphones or wearable device, as known in the art to communicate with thesystem 300.

The system 300 includes, for example and without limitation, server 305.Server 305 may include a messaging server, which may be used to receiveand send data via email or over the Internet 131. The system 300 may usevarious attribute data in the Electronic Data Interchange (EDI) formatfor electronic tracking of specific data as discussed in the foregoing.Server 305 can process an EDI messages sent through the processingsystem 300 to improve computer processing and machine learningfunctionality to thereby bring new tangible improved functions to thetechnology area of automatic AI recommendation to the user of device 501based on GPS location and other data. A user with device 301 a-301 c and302 a-302 c may securely register to system 300 via a website URLregistration service, an in-person registration service, a mail-inregistration service, and/or some other registration service. Abiometric device system may be included to allow for scanning of an irisof the user, retina scan, face recognition, and/or other types ofbiometric identification and authentication, including fingerprint scananalysis.

FIGS. 3A-3C are merely illustrative and the number of, users and/or userterminals, servers and databases is not in any way limited. Furthermore,although various embodiments are described in the context of a singlesystem, one of ordinary skill in the art may appreciate that thedescribed functionality may be implemented across multiple systems.Moreover, a web site may be mirrored at additional systems in thenetwork and, if desired, one or more management systems or othercomputer resources may be used to facilitate various functions. Thecomputer program at the system 300 includes appropriate screen routinesfor generating a set of screens that together comprise a graphical userinterface for the site.

The system 300 can provide messages in the communication cycle in anauthenticated format, secure for each user that would be invited orauthorized to be a part of the secure exchange. For example, an IPSECcircuit is commonly to pertain to IP Security, a set of protocols tosupport secure exchange of packet at the IP layer in a TCP/IP networksystem. IPSEC systems have been deployed widely to implement VirtualPrivate Networks (VPNs). Under an IPSEC system, at least two encryptionmodes are supported: Transport and Tunnel. Transport mode encrypts onlythe payload portion of each packet, but leaves the header. The Tunnelmode encrypts both the header and the payload. On the receiving side, anIPSEC-compliant device decrypts each packet. The methods and featuresrecited herein further may be implemented through any number ofnon-transitory computer readable media that are able to store computerreadable instructions. Examples of non-transitory computer readablemedia that may be used include RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, DVD, or other optical disc storage, magneticcassettes, magnetic tape, magnetic storage and the like.

According to some aspects of the disclosure, a machine learning neuralnetwork processing system 300 provides various users efficiency datacommunication tracking and delivery of a neural network recommendationsbased on data obtained from the GPS location of device 501 (see FIG. 5).Device 501 may include a global position system device/electronics 500providing mapping coordinates. In one construction, the machine learningsystem 300 provides electronic messaging back and forth that inputsrequests for selective location data retrieved from the GPS device (seeFIG. 4).

Referring to FIGS. 3A and 3B, in one aspect, system 300 may use variousattribute data in the Electronic Data Interchange (EDI) format. In oneimplementation, the EDI message can use, for example, IPSEC circuity forsecure encrypted communications. The EDI messages can be electronicallyprocessed according any number of formats and data sequences. In onecase, the EDI format and payload, may include an neural net node IDnumber 315, a GPS attribute 317, a data element A attribute 319, and adata element B attribute data 321, and biometric data attribute data323. The attribute data 317 pertains to a determined location, such asLocations 1 through 3 in FIG. 4. Biometric ID data attribute data 323pertains to user biometric information. The GPS location associated withGPS attribute 323 may have at least the longitude and latitude of thelocation to linked to a mapping application. In the EDI format andpayload, the “attribute data” may include ASCII characters in computerreadable form or binary complied data, such as biometric data. The ASCIIcharacters or binary data can be manipulated in the software of system300. In one construction, a temporary virtual node with virtualsub-nodes may be electronically linked to the node ID attribute 315.Referring to FIG. 3C, neural network 303 is generally arranged in“layers” of node processing units serving as simulated neutrons, suchthat there is an input layer 308, representing the input fields into thenetwork. To provide the automated machine learning processing, one ormore hidden layers 309 with machine learning rule sets processes theinput data. An output layer 311 provides the result of the processing ofthe network data.

The steps that follow in the FIG. 4 may be implemented by one or more ofthe components in FIGS. 1, 2 and 3A-3B and/or other components,including other computing devices. Referring to FIG. 4, in a generaloverview, system 300 electronically maintains a plurality of electronicnodes for users associated in a computer readable electronic database121. In such an example, the separate node data 314 may be stored withina non-transitory computer readable memory/database, such as memory 115and/or RAM 105 in FIG. 1 or electronic database 307 of FIGS. 3A-3B. Auser with device 301 a-301 c electronically communicates with system 300and devices 302 a-302 c via system 300.

The steps that follow in FIG. 4 can be implemented to include a computerreadable transaction history or log of the status within process flowsthat can be maintained or otherwise stored within a memory/database,such as memory 115 and/or RAM 105 in FIG. 1 or electronic database 307of FIGS. 3A-3C. In one construction, the steps that follow in the FIG. 4can be implemented where the vendor, customer or other entity canreceive inquiries, via an automatic push notification or a report thatsends to the authorized inquirer an electronic mail, text messaging viaShort Messaging Service (SMS) component of phone, web, or mobilecommunication systems, using standardized communication protocols thatallow the exchange of short text messages between fixed line or mobilephone devices. In another manner, the customer or entities can receiveinquiries via a pull format where the inquirer initiates the query atvarious steps and the notification can be electronic mail or ShortMessaging Service technology for cellular phones.

Referring to FIG. 4 process flow of system 300, the various items inSteps S0-S3, are electronic logically via computer readable instructionslinked to electronic attribute data for EDI message processing viasystem 300. A user travels with device 501 and communicates withprocessing system 300 as discussed in the foregoing. The system 300 maythen employ selective attribute data in the Electronic Data Interchange(EDI) format to form a tokenized data form of transfer. For example, inStep S0, a temporary virtual node 314 is created in the memory/database307 with at least one node ID number 315, and GPS location attribute317, a data element A attribute 319, a data element B attribute 321 anda biometric ID attribute 323 of the user can be optionally provided inthe temporary virtual node 314. In one example at location S0, dataelement A 319, data element B 321 can be associated with a data of arecent physical acquisition of an article of manufacture linked to theGPS location (e.g., a grocery store). Alternatively, data element A 319,data element B 321 can be associated with a data of recent product ortransaction linked to the GPS location (e.g., bank location).

In Step S1, the device 501 has arrived at Location 1. Referring to FIG.3B, a temporary virtual node 330 is created in the memory/database 307with at least one node ID number 330, and GPS location attribute 317, adata element C attribute 325, a data element D attribute 327 and abiometric ID attribute 323 of the user can be optionally provided in thetemporary virtual node 330. In one example at Location 1, data element C325, data element B 327 can be associated with data linked to the GPSlocation (e.g., medical facility). Alternatively, data element A 319,data element B 321 can be associated with a data of recent productpurchase linked to the GPS location (e.g. auto dealer). Likewise, inSteps S2 and S3 as the device 502 moves from Location 2 to Location 3, atemporary a unique virtual node is created in the memory/database 307with at least one node number, and a GPS location attribute, differentdata element attributes are stored for later use in the neural network303. As noted, device 501 moves between various locations and the dataelements created at the nodes may be provided to neural network 303 toprovide machine learning based recommendations. For example, device 501could travel from Location 1 directly to Location 3. Likewise, device501 can travel directly to Location 2 or Location 3.

Referring to FIG. 3C, data element A attribute 317, data element Battribute 319, data element C attribute 325, data element D attribute327, serves as input data into input layer 308, representing the inputfields into the neural network 303. To provide the automated learningprocessing, one or more hidden layers 309 with machine learning rulesets processes the noted input data associated with data elements A-D.The output layer 311 provides the result of the processing of thenetwork data to the user device 501. Referring to FIG. 7, in the outputlayer 311 can provide various alternative one or more recommendationsbased on data elements A-D and the recommendations RECO 401-403 can beconverted into graphical screen display characters or images for use ondevice 501. It is contemplated that RECO 401-403 can be provided via anelectronic mail, text messaging via Short Messaging Service (SMS)component of phone, web, or mobile communication systems, usingstandardized communication protocols that allow the exchange of shorttext messages between fixed line or mobile phone devices. Therecommendations RECO 401-405 can be in the form of many types includingnavigation information to frequent terrestrial locations of the user ofthe device 501; similar or related article of manufacture information;similar or related services information; or health/wellness information,such as fitness facility locations, etc.

Referring to FIG. 6, in some embodiments, data elements A-D can beorganized into a smart data set, such that the multiple numbers of nodeswith GPS data can provide benefits to the users 301 a-301 c with device501. For example, multiple nodes, which connect to more than a thresholdnumber of nodes, can be determinative of which data elements to provideto the neural network 303 to provide a focused recommendation to theuser, that is, noise data elements can be excluded for processing. Inone case, certain nodes could be associated with a specific GPS locationthat has threshold number of nodes, while another GPS location node maybe only one entry over a time period. The threshold number of nodeswould most likely be representative of an activity or location of somefrequent use by the user. The GPS data can be one method of a taggingcharacteristic to determine the threshold value or a frequent dataelement attribute value in data elements A-D can be another way oftagging. In the example of FIG. 6, the smart data set includes dataelement B attribute 319, data element C attribute 325, data element Dattribute 327 which data element A attribute in the large set has beenexcluded.

Referring to FIG. 8, data element B attribute 319, data element Cattribute 325, data element D attribute 327, serves as input data intoinput layer 308, representing the input fields into the neural network303. The output layer 311 provides the result of the processing of thenetwork data to the user device 501. In this embodiment, referring toFIG. 7, in the output layer 311 can provide various alternative one ormore recommendations based on data elements B-D and the recommendationsRECO 401, 403 can be converted into graphical screen display charactersor images for use on device 501. In this case, RECO 401, 403 has beenprovided due and not RECO 405 to illustrate that the smart data canprovide enhanced recommendation output from neural network 303.

While illustrative systems and methods as described herein embodyingvarious aspects of the present disclosure are shown, it will beunderstood by those skilled in the art, that the disclosure is notlimited to these embodiments. Modifications may be made by those skilledin the art, particularly in light of the foregoing teachings. Forexample, each of the elements of the aforementioned embodiments may beutilized alone or in combination or sub-combination with elements of theother embodiments. It will also be appreciated and understood thatmodifications may be made without departing from the true spirit andscope of the present disclosure. The description is thus to be regardedas illustrative instead of restrictive on the present disclosure.

The invention claimed is:
 1. An electronic computer implemented methodof data communication, comprising: via a computer-based network,receiving a plurality of virtual nodes with EDI data payload including anode attribute, a GPS location attribute and a biometric ID attributeand at least one data element associated with the GPS locationattribute; electronically via a data communications network, processingthe EDI data payloads including the node attribute, the GPS locationattribute and the biometric ID attribute and the at least one dataelement associated with the GPS location attribute and outputting asubset of the EDI data payloads to define a smart data set;electronically processing the smart data set in a network with machinelearning and providing an electronic message responsive thereto; andtransmitting via an EDI data payload, the electronic message to a deviceassociated with the biometric ID attribute.
 2. One or morenon-transitory computer readable media storing computer executableinstructions that, when executed by at least one processor, cause the atleast one processor to perform a data communication method, comprising:via a computer-based network, receiving a plurality of virtual nodeswith EDI data payload including a node attribute, a GPS locationattribute and a biometric ID attribute and at least one data elementassociated with the GPS location attribute; electronically via a datacommunications network, processing the EDI data payloads including thenode attribute, the GPS location attribute and the biometric IDattribute and the at least one data element associated with the GPSlocation attribute and outputting a subset of the EDI data payloads todefine a smart data set; and electronically processing the the smartdata set in a network with machine learning and providing an electronicmessage responsive thereto; and transmitting via an EDI data payload,the electronic message to a device associated with the biometric IDattribute.
 3. A digital computer system, comprising: at least onecomputer readable database configured to maintain a plurality ofcomputer readable nodes; and at least one computing device, operativelyconnected to the at least one computer readable database, configured to:electronically receive a plurality of virtual nodes with EDI datapayload including a node attribute, a GPS location attribute and abiometric ID attribute and at least one data element associated with theGPS location attribute; electronically process the EDI data payloadsincluding the node attribute, the GPS location attribute and thebiometric ID attribute and the at least one data element associated withthe GPS location attribute and output a subset of the EDI data payloadsto define a smart data set; electronically process the smart data set ina network with machine learning and providing an electronic messageresponsive thereto; and transmit via an EDI data payload, the electronicmessage to a device associated with the biometric ID attribute.